A Follower agent model is tasked with inferring the whole sequence of Follower environmental actions taken during the session conditioned on the dialogue history.
A TfD instance is \(\left( S_i, A_H^D, A_R^I, S_f \right)\) , where \(A_H^D\)  is all dialogue actions taken by both agents, and \(A_R^I\)  is all non-dialogue actions taken by the Follower.
We append a Stop action to \(A_R^I\) .
The agent does not observe dialogue actions in context,
however, we
use this task to test long horizon action prediction with a block of instructions, analogous to ALFRED or TouchDown [1]}.
We calculate success and goal-conditioned success by comparing \(\hat{E}\)  against state changes between \(S_i\)  and \(S_f\) .
